Green vs. Green:
Measuring the Compensation Required to Site
Electrical Generation Windmills in a Viewshed
Peter A. Groothuis
Department of Economics
Appalachian State University
Boone, NC 28608
Jana D. Groothuis
Boone, NC 28608
and
John C. Whitehead
Department of Economics
Appalachian State University
Boone, NC 28608
June 2007
ABSTRACT: A willingness to accept framework is used to measure the compensation
required to allow wind generation windmills to be built in the mountains of North
Carolina. We address why the NIMBY syndrome may arise when choosing site locations,
the perceived property rights of view-sheds, as well as the perceptions of the status quo in
the southern Appalachian Mountains. We find that individuals who perceive wind
energy as a clean source of power require less compensation. Those who retire to the
mountains or individuals who have ancestors from Watauga County require more
compensation to accept windmills in their view-shed. We find that annual compensation
is about twenty three dollars per household. In the aggregate, citizens need to be
compensated by about one-half million dollars a year to allow wind electrical generation
turbines in Watauga County. In addition, we find in a bivariate-probit analysis that
individuals who are more likely to participate in a green energy program also are more
likely to allow electrical generation wind mills in their view-shed suggesting that the
green on green environmental debate is overstated.
2
Introduction
Wind electrical generation is becoming an economically viable technique.
Proponents say that wind power is both a green and sustainable energy source. Green
energy is defined as a source that does not contribute to global warming or other negative
externalities such as acid rain or decreased visibility (Kahn 2003). Borchers et al (2007)
using a choice experiment find that consumers are willing to pay more for green energy
including wind, solar and biomass relative to conventional sources. Whitehead and
Cherry (forthcoming) using the contingent valuation method suggest that individuals are
willing to pay a premium to purchase green energy knowing that their choice leads to a
cleaner environment. Roe et al. (2001) using the hedonic study find consumers are
willing to pay a premium for renewable energy sources.
Those who oppose wind energy, however, point out that wind generation turbines
can cause local negative externalities. Individuals who live near wind generation turbines
have reported negative visual impact on the landscape, noise from the rotation of
windmill blades and shadow and light effects from the windmills (Warren et al. 2005).
Landenburg and Dubgaard (2007) find that individuals are willing to pay higher electrical
bills to site coastal wind farms further from the coast. The two sides to this wind power
debate have been labeled by some as a green on green environmental debate. Proponents
focus on the clean air and opponents focus on the local negative externalities (Warren et
al. 2005). One recent example of the green on green debate is the Cape Cod coastal wind
farms where a slim majority of respondents were opposed to wind farms (Coleman and
Melo. 2004). Other examples are detailed in Pasqualetti (2004) where individuals argue
that pristine landscapes will be damaged by windmills.
3
.
Local negative externalities from wind turbines fall into the broad problem known
as a locally undesirable land use, or LULU. In turn, LULUs lead to the NIMBY (not in
my backyard) syndrome. Economists theorize that the NIMBY syndrome leads to
inefficient allocation of resources because the external costs of a LULU are borne locally
by the neighborhood surrounding the facility, while the benefits are distributed globally
throughout the economy (O'Hare 1977, Kunreuther et al 1987). To address the problem
of inefficiency and to encourage the placement of a LULU, those that receive the benefits
could compensate the neighborhood around the site for bearing the external cost (O'Hare
1977 and Kunreuther et al 1987).
To gain understanding of citizen’s perceptions of wind power and their influence
on mountain views we developed a contingent valuation (CV) survey. The CV analysis
provides insights on how to overcome the NIMBY syndrome by eliciting the willingness
to accept (WTA) for changes in the county’s view-shed amenities of wind turbines.
Although the willingness to pay framework is the preferred question format in CV
analysis, the WTA framework is more appropriate given the perceived property rights of
individuals in the current context. Individuals perceive that the status quo defines
property rights, making the WTA the appropriate measure. CV analysis has been used
successfully in the past to measure the WTA (See Groothuis, Van Houtven, and
Whitehead 1998, and Carson, Flores and Meade 2001).
The survey was sent to Watauga County, North Carolina, residents. This County,
located in the southern Appalachian highlands, offers an interesting case study because it
has been identified as a potential wind farm location and is also noted for its scenic views
4
(Grady 2002). The Blue Ridge Parkway runs through the county. Many vacation homes
are built in the area and tourism is a major industry. Watauga County also has had
decreased visibility due to coal powered electrical generation plants and would benefit
from the green energy that wind power would provide (Grady 2002).
Section 1: Survey Methodology and Results
The survey was mailed in the spring of 2005 to a random sample of 1200
Watauga County residents. We used a primary mailing, a post card reminder and a
second mailing to all non-respondents of the first wave. In the end, we had 901 useable
addresses and 389 responses giving us a response rate of 43 percent. In table 1, we
provide a summary of the demographic variables. The average age of our respondent
was 55.7 years, while the average age for the county of all residents over 20 was 43.5.
The average income of survey respondents was $61,084 1 while the average income in
Watauga County from the 2000 census was $50,300 in 2005 dollars. The average level
of education for the respondents was 15 years and for the county it was 14 years. The
respondents are older, slightly more educated and have higher income than the
population. In addition eleven percent of the respondents have retired to Watauga
County and 33 percent report having ancestors who lived in Watauga County.
In order to compare respondent attitudes, in tables, 2, 3 and 4, we summarize the
opinions of the usefulness and impact on mountain views of various technologies that
could cause externalities. These include cell phone towers, billboards and electrical
generation wind mills. We find that 46 percent of respondents find that billboards
provide somewhat useful information and 42 percent use billboards to make decisions on
5
where to shop and eat when they visit other locations. Yet around 80 percent either find
that billboards are somewhat harmful to very harmful to the mountain views of Watauga
County.
When it comes to cell phone towers, the vast majority, 91%, find that the cell
phone service is somewhat useful to very useful. Yet, 67% feel that cell towers are
somewhat harmful to very harmful to mountain views. A majority of respondents also
feel that wind energy is a clean energy that should be pursued. Yet, 64% feel that
electrical generation wind mills will harm mountain views. This series of tables show
respondents find that billboards, cell phone towers, and wind electrical power all provide
benefits. They also feel that they all harm mountain-view amenities, suggesting that
tradeoffs need to be made.
Section 2: Participation in Green Energy Programs
Wind energy provides benefits to citizens of the Appalachian Mountains. To help
understand who receives the benefits, we ask about participation in a Green energy
program. In the survey the following statement was included:
A Green Energy proposal has been implemented in North Carolina. Green
Energy offers customers power generated from renewable sources such as wind
or solar power at a higher price than electricity generated with coal or oil.
Green energy reduces air pollution in the mountains and improves visibility. Do
you participate or plan to participate in the Green Energy program in North
Carolina? YES or NO
We find that 35.8% of respondents report that they participate or plan to
participate in the green energy program. To further explore who participates, a logit
analysis is used to determine the willingness to participate in the green energy program.
Reported in table 5, we find that older individuals are less likely to participate than
6
younger individuals. We also show that individuals who report ancestors who lived in
Watauga County are less likely to participate in a green energy program than individuals
who did not have ancestors who lived in Watauga County. In terms of the attitudinal
variables, we find that individuals who strongly agree or agree that wind power is clean
and should be pursued were more likely to participate in the green energy program. The
coefficient on the variables Wind-power-is-harmful-to-mountain-views and Windenergy-should-be-allowed-in-Watauga County are both statistically insignificant. The
results suggest that some individuals in Watauga County are willing to pay more to
receive the global benefits of green power.
Section 3: Willingness to Accept for Electrical Generation Windmills
Wind energy creates negative externalities for citizens of the Appalachian
Mountains when the wind mills are built in the view-shed. We analyze a CV question to
provide insight on how to overcome the NIMBY syndrome by providing compensation to
the affected parties. Using this technique we can elicit the willingness to accept (WTA)
for changes in the county’s view-shed amenities from wind turbines.
Theory:
Consider a resident’s utility function who receives utility from both a
consumption good, z and a scenic view amenity, x(q), where q represents quality of the
scenic amenity that can be affected by the presence of windmills. Then a resident
maximizes her utility, u(x(q),z), subject to a budget constraint y=px+z where the price of
z is normalized to one. Solving for the indirect utility function yields v(p,y,q) where p
7
represents the price of the scenic amenity and y is income. The willingness-to-accept,
WTA, for lowering the quality of a scenic view amenity is found when,
1) v(p0, q0,y) = v(p0, q1 y + WTA ),
where p0 is the current price, q1 is lowered quality and WTA is the willingness-to-accept
compensating variation for lowering scenic view quality.
In our case the CV question for the windmill proposal is:
Suppose, to generate Green electricity, windmill generators are to be built on four
ridge tops throughout Watauga County. To compensate individuals in the county
for accepting windmills, electric utility bills would be reduced by $A each month
per household. Suppose that this proposal, approving the electrical payment
reduction and allowing electrical windmills to be built, is on the next election
ballot. How would you vote on this proposal?
FOR AGAINST DON’T KNOW
One problem that arises when estimating dichotomous choice CV questions is
what to do with Don’t Know responses. We follow the status quo approach and code all
Don’t Know responses as “No” responses (Caudill and Groothuis 2004 and Groothuis
and Whitehead 2002). This becomes our variable that we label Yes.
In table 6, we report the percentage of votes by offer. We find for the lowest offer
of one dollar per month, only 42% would vote for the proposal but increasing the offer to
$2.50 per month 53% were in favor of the wind proposal. For higher offer levels of
$5.00, $10.00 and $50.00 over 60 percent would vote for the wind proposal. In Table 7
column 1, we report the results of two specifications on the wind proposal.
2) P(Yes) = β0 + β1log(A) + β2 Income + ε2
3) P(Yes) = β0 + β1log(A) + β2 Income + β3Education + β4Age + β5Clean +
β6Harm +β7Allowed + β8Ancestor + β9 Retire + ε2
8
In both specifications as the log of payment A increases respondents are more
likely to be in favor of the proposal. In the first specification, the coefficient on the
income variable is found to be negative and statistically significant, indicating that
landscape is a normal good. In the second specification all three attitudinal variables are
found to have statistically significant coefficients. Individuals who find wind power a
clean source of power that should be pursued are more likely to vote for the proposal.
Individuals who find wind power harmful to mountain views are less likely to say yes.
Also individuals who believe wind power should be allowed are more likely to vote in
favor of the proposal. Individuals who retire to Watauga County are less likely to say
yes, while individuals with ancestors in the county are also less likely to say yes to the
proposal. In the second specification, we find that age, education and income are all
have statistically insignificant coefficients. These results point to compensation playing a
role in solving the NIMBY syndrome.
Using the Cameron and James (1987) technique we can calculate the median
WTA using:
4) WTA = exp((β0 + β2 Income + β 3Education + β4Age + β5Clean +
β6Harm +β7Allowed + β8Ancestor + β9 Retire)/ β1).
We find that in the WTA is $1.90 per month or a compensation of $23 per household per
year.
To further explore the green on green environmental debate, where individuals
focus on either the positive benefits or the local negative externalities of wind power, we
analyze a bivariate-probit analysis of both the willingness to participate in a Green
Energy program and the willingness to accept windmills in Watauga County. In table 8,
9
the results mirror those of both univariate analyses. The interesting result is that the rho
(ρ) coefficient that measures the unobserved correlation between the two equations is
positive and statistically significant. Individuals who respond yes to the green-energy
participation equation are also more likely to respond yes to the willingness to accept
windmills equation. This result indicates that the green on green conflict does not
necessarily arise in the same individual. Instead, individuals who are willing to pay more
for green energy are also more likely to allow windmills into their view-shed. This runs
counter to the conventional wisdom that individuals who are concerned with the
environment are willing to pay for clean air as long as the wind energy is not produced in
their view-shed. On the contrary, individuals who are willing to pay more for green
energy require less compensation to site windmills in their backyard.
Section 5: Conclusions
Our results indicate that compensation could play a role to help eliminate the
NIMBY syndrome when choosing sites for electrical generation windmills. We also find
that individuals who are willing to participate in green energy programs require less
compensation than individuals who do not participate. This result suggests that the green
on green environmental debate is overstated. In addition, we show that individuals who
retire to the mountains require more compensation as do individuals who have ancestors
in the county. The landscape is important to citizens of Watauga County but they are
willing to make trade offs to allow for wind power.
To gain an understanding of the total compensation required to site wind turbines
in Watauga County, we can aggregate the household results of 23 dollars annually.
Using the 2000 census we find that there are 18,540 households in Watauga County
10
giving an aggregate WTA of $426,400 dollars per year. This estimate can be thought as
the amount that would be approved in a referendum election -- reflecting the preferences
of a median voter. Thus it would be the amount required to eliminate the NIMBY
syndrome and site wind turbines.
11
References
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for Green Energy Differ by Source?” Energy Policy, 35, 3327-3334, 2007.
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269-276, 1987.
Carson, Richard T., Nicholas E. Flores and Norman F. Meade, ‘Contingent Valuation:
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210 2001.
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Utility Models: An Application to “Don’t Know” Responses in Contingent
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to Measure the Compensation Required to Gain Community Acceptance of a
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'Don't Know Responses in Dichotomous Choice Contingent Valuation
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Disamenities from Off-Shore Windfarms in Denmark” Energy Policy 4059-4071
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Importance of Compensation, Public Policy 25:4, 407-58 1977.
12
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13
Table 1 Means of Variables
Variable
Mean
Age
55.7 years
(16.0)
Income
$61,084
(33.97)
Education
15.09 years
(3.81)
Wind powers should be
allowed in Watauga
County1
.61
(.48)
Ancestor from Watauga
County
.33
(.47)
Retire to Watauga
.16
(.37)
(Standard deviation in parentheses)
1
Electical generation wind mills should be allowed in Watauga County.
Strongly Agree or Agree equal one
Strongly Disagree or Disagree equal zero
n=337
14
Table 2: Opinions about Billboards
Do you feel billboards
provide useful information
to tourists and residents?
Do you feel that billboards
are harmful to mountain
views?
Do you use billboards to
make decisions on where
to shop and eat when you
visit other locations?
1
Not At All
Useful
2
3
Somewhat
Useful
4
5
Very Useful
14.9%
Not at all
Harmful
22.5%
46.3%
Somewhat
Harmful
7.8%
8.3%
Very
Harmful
9.4%
Never
8.9%
32.5%
Some of the
Time
18.3%
30.9%
All the time
27.2%
16.2%
42.4%
6.8%
7.3%
Table 3: Opinions about Cell Phone Towers
Do you feel cell phone
coverage provides useful
service for cell phone
owners such as
convenience and safety?
Do you feel that cell phone
towers are harmful to the
mountain views?
1
Not At All
Useful
2
3
Somewhat
Useful
4
5
Very Useful
2.5%
6.3%
24.4%
25.2%
41.6%
Not at all
Harmful
13.4%
Somewhat
Harmful
18.9%
39.7%
Very
Harmful
16.1%
11.7%
Table 4: Opinions about Electrical Generation Wind Mills
Do you feel electrical that
generation wind mills are a
clean energy source that
should be pursued in the
future?
Do you feel that electrical
generation wind mills are
harmful to the mountain
views?
1
Should Not
Be Pursued
2
3
4
5
Should Be
Pursued
5.8%
5.8%
22.5%
18.1%
48.0%
Not at all
Harmful
15.1%
Somewhat
Harmful
20.8%
15
42.2%
Very
Harmful
12.6%
9.3%
Table 5: Determinants of Willingness to Participate in Green Energy Program
Variable
Participate
Intercept
Wind power is clean and
should be pursued
-1.14
(1.04)
.001
(0.33)
-.103
(0.26)
-.357**
(3.44)
.810**
(4.66)
Wind power harmful to
mountain views
-.040
(0.29)
Wind power should be
allowed in Watauga County
-.073
(0.12)
Ancestor in Watauga
-1.11**
(3.48)
.075
(0.09)
149.21**
Income
Education
Age
Retired to Watauga
χ2
16
Table 6: Percentage of respondents votes of wind proposal by offer
Offer
Yes
no
Total
$1.00
42%
58%
78
$2.50
53%
47%
64
$5.00
65%
35%
73
$10.00
67%
33%
64
$50.00
67%
33%
48
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Table 7: Determinants of Willingness to Accept Electrical Windmills
Yes
Variable
Yes
Intercept
0.56*
(1.77)
.224**
(3.65)
-1.93
(1.73)
.448**
(3.65)
-.0067*
(1.86)
Wind power is clean and
should be pursued
-.0064
(1.31)
-.103
(0.25)
-.165
(0.17)
.635**
(4.30)
Wind power harmful to
mountain views
-.289**
(2.18)
Wind power should be
allowed in Watauga County
1.60**
(5.00)
Ancestor in Watauga
Log likelihood Ratio
-.944**
(2.82)
-.947**
(2.20)
153.81**
Median WTA
(95% Confidence Interval)
$1.90
($.50 to $3.30)
Log of Electrical Bill
Reduction
Income
Education
Age
Retired to Watauga
*statistically significant at 90% level. **statistically significant at 95% level
18
Table 8: Bivarite Probit: Willingness to Accept Electrical Windmills and
Willingness to Participate to Participate in Green Energy Program
Variable
Yes
Yes
Accept Windmills
Participate Green Energy
Intercept
-1.081
-.710
(1.67)
(1.16)
Log of Electrical Bill
0.254*
--Reduction
(3.74)
Income
Wind power is clean and
should be pursued
-.003
(1.26)
-.006
(0.25)
-.001
(0.12)
.354*
(4.38)
.001
(0.32)
-.166
(0.71)
-.020*
(3.76)
.421*
(4.72)
Wind power harmful to
mountain views
-.169*
(2.44)
.030
(0.39)
Wind power should be
allowed in Watauga County
.984*
(5.08)
.051
(0.26)
Ancestor in Watauga
-.558*
(2.76)
-.583*
(2.27)
.406*
(3.70)
-331.56*
-.653*
(3.44)
.005
(0.02)
Education
Age
Retired to Watauga
Rho
Log likelihood Ratio
*statistically significant at 95% level.
19
1
Income tends to have the most item non-response of all demographic questions. Following Whitehead
(1994) we impute 18 missing wage values using a wage equation.
20